Class LatentDirichletAllocationScikitsLearnNode

Latent Dirichlet Allocation with online variational Bayes algorithm
This node has been automatically generated by wrapping the ``sklearn.decomposition.online_lda.LatentDirichletAllocation`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
.. versionadded:: 0.17
**Parameters**
n_topics : int, optional (default=10)
Number of topics.
doc_topic_prior : float, optional (default=None)
Prior of document topic distribution `theta`. If the value is None,
defaults to `1 / n_topics`.
In the literature, this is called `alpha`.
topic_word_prior : float, optional (default=None)
Prior of topic word distribution `beta`. If the value is None, defaults
to `1 / n_topics`.
In the literature, this is called `eta`.
learning_method : 'batch' | 'online', default='online'
Method used to update `_component`. Only used in `fit` method.
In general, if the data size is large, the online update will be much
faster than the batch update.
Valid options::
'batch': Batch variational Bayes method. Use all training data in
each EM update.
Old `components_` will be overwritten in each iteration.
'online': Online variational Bayes method. In each EM update, use
mini-batch of training data to update the ``components_``
variable incrementally. The learning rate is controlled by the
``learning_decay`` and the ``learning_offset`` parameters.
learning_decay : float, optional (default=0.7)
It is a parameter that control learning rate in the online learning
method. The value should be set between (0.5, 1.0] to guarantee
asymptotic convergence. When the value is 0.0 and batch_size is
``n_samples``, the update method is same as batch learning. In the
literature, this is called kappa.
learning_offset : float, optional (default=10.)
A (positive) parameter that downweights early iterations in online
learning. It should be greater than 1.0. In the literature, this is
called tau_0.
max_iter : integer, optional (default=10)
The maximum number of iterations.
total_samples : int, optional (default=1e6)
Total number of documents. Only used in the `partial_fit` method.
batch_size : int, optional (default=128)
Number of documents to use in each EM iteration. Only used in online
learning.
evaluate_every : int optional (default=0)
How often to evaluate perplexity. Only used in `fit` method.
set it to 0 or and negative number to not evalute perplexity in
training at all. Evaluating perplexity can help you check convergence
in training process, but it will also increase total training time.
Evaluating perplexity in every iteration might increase training time
up to two-fold.
perp_tol : float, optional (default=1e-1)
Perplexity tolerance in batch learning. Only used when
``evaluate_every`` is greater than 0.
mean_change_tol : float, optional (default=1e-3)
Stopping tolerance for updating document topic distribution in E-step.
max_doc_update_iter : int (default=100)
Max number of iterations for updating document topic distribution in
the E-step.
n_jobs : int, optional (default=1)
The number of jobs to use in the E-step. If -1, all CPUs are used. For
``n_jobs`` below -1, (n_cpus + 1 + n_jobs) are used.
verbose : int, optional (default=0)
Verbosity level.
random_state : int or RandomState instance or None, optional (default=None)
Pseudo-random number generator seed control.
**Attributes**
``components_`` : array, [n_topics, n_features]
Topic word distribution. ``components_[i, j]`` represents word j in
topic `i`. In the literature, this is called lambda.
``n_batch_iter_`` : int
Number of iterations of the EM step.
``n_iter_`` : int
Number of passes over the dataset.
**References**
[1] "Online Learning for Latent Dirichlet Allocation", Matthew D. Hoffman,
David M. Blei, Francis Bach, 2010
[2] "Stochastic Variational Inference", Matthew D. Hoffman, David M. Blei,
Chong Wang, John Paisley, 2013
[3] Matthew D. Hoffman's onlineldavb code. Link:
- http://www.cs.princeton.edu/~mdhoffma/code/onlineldavb.tar

Latent Dirichlet Allocation with online variational Bayes algorithm
This node has been automatically generated by wrapping the ``sklearn.decomposition.online_lda.LatentDirichletAllocation`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.
.. versionadded:: 0.17
**Parameters**
n_topics : int, optional (default=10)
Number of topics.
doc_topic_prior : float, optional (default=None)
Prior of document topic distribution `theta`. If the value is None,
defaults to `1 / n_topics`.
In the literature, this is called `alpha`.
topic_word_prior : float, optional (default=None)
Prior of topic word distribution `beta`. If the value is None, defaults
to `1 / n_topics`.
In the literature, this is called `eta`.
learning_method : 'batch' | 'online', default='online'
Method used to update `_component`. Only used in `fit` method.
In general, if the data size is large, the online update will be much
faster than the batch update.
Valid options::
'batch': Batch variational Bayes method. Use all training data in
each EM update.
Old `components_` will be overwritten in each iteration.
'online': Online variational Bayes method. In each EM update, use
mini-batch of training data to update the ``components_``
variable incrementally. The learning rate is controlled by the
``learning_decay`` and the ``learning_offset`` parameters.
learning_decay : float, optional (default=0.7)
It is a parameter that control learning rate in the online learning
method. The value should be set between (0.5, 1.0] to guarantee
asymptotic convergence. When the value is 0.0 and batch_size is
``n_samples``, the update method is same as batch learning. In the
literature, this is called kappa.
learning_offset : float, optional (default=10.)
A (positive) parameter that downweights early iterations in online
learning. It should be greater than 1.0. In the literature, this is
called tau_0.
max_iter : integer, optional (default=10)
The maximum number of iterations.
total_samples : int, optional (default=1e6)
Total number of documents. Only used in the `partial_fit` method.
batch_size : int, optional (default=128)
Number of documents to use in each EM iteration. Only used in online
learning.
evaluate_every : int optional (default=0)
How often to evaluate perplexity. Only used in `fit` method.
set it to 0 or and negative number to not evalute perplexity in
training at all. Evaluating perplexity can help you check convergence
in training process, but it will also increase total training time.
Evaluating perplexity in every iteration might increase training time
up to two-fold.
perp_tol : float, optional (default=1e-1)
Perplexity tolerance in batch learning. Only used when
``evaluate_every`` is greater than 0.
mean_change_tol : float, optional (default=1e-3)
Stopping tolerance for updating document topic distribution in E-step.
max_doc_update_iter : int (default=100)
Max number of iterations for updating document topic distribution in
the E-step.
n_jobs : int, optional (default=1)
The number of jobs to use in the E-step. If -1, all CPUs are used. For
``n_jobs`` below -1, (n_cpus + 1 + n_jobs) are used.
verbose : int, optional (default=0)
Verbosity level.
random_state : int or RandomState instance or None, optional (default=None)
Pseudo-random number generator seed control.
**Attributes**
``components_`` : array, [n_topics, n_features]
Topic word distribution. ``components_[i, j]`` represents word j in
topic `i`. In the literature, this is called lambda.
``n_batch_iter_`` : int
Number of iterations of the EM step.
``n_iter_`` : int
Number of passes over the dataset.
**References**
[1] "Online Learning for Latent Dirichlet Allocation", Matthew D. Hoffman,
David M. Blei, Francis Bach, 2010
[2] "Stochastic Variational Inference", Matthew D. Hoffman, David M. Blei,
Chong Wang, John Paisley, 2013
[3] Matthew D. Hoffman's onlineldavb code. Link:
- http://www.cs.princeton.edu/~mdhoffma/code/onlineldavb.tar

_stop_training(self,
**kwargs)

execute(self,
x)

Transform data X according to the fitted model.

This node has been automatically generated by wrapping the sklearn.decomposition.online_lda.LatentDirichletAllocation class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.

is_trainable()Static Method

stop_training(self,
**kwargs)

Learn model for the data X with variational Bayes method.

This node has been automatically generated by wrapping the sklearn.decomposition.online_lda.LatentDirichletAllocation class
from the sklearn library. The wrapped instance can be accessed
through the scikits_alg attribute.

When learning_method is 'online', use mini-batch update.
Otherwise, use batch update.